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Statistical Software

Getting Started with SPSS Syntax

by Karen Grace-Martin  Leave a Comment

spss-logoYou may have heard that using SPSS syntax is more efficient, gives you more control, and ultimately saves you time and frustration.  It’s all true.

….And yet you probably use SPSS because you don’t want to code.  You like the menus.

I get it.

I like the menus, too, and I use them all the time.

But I use syntax just as often.

At some point, if you want to do serious data analysis, you have to start using syntax.  [Read more…] about Getting Started with SPSS Syntax

Tagged With: SPSS, spss syntax, Statistical Software

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Three SPSS Shortcuts that Make Life Easier

by Karen Grace-Martin  1 Comment

Okay, maybe these SPSS shortcuts won’t make your whole life easier, but it will help your work life, at least the SPSS part of it.

When I consult with researchers, a common part of that is going through their analysis together.  Sometimes I notice that they’re using some shortcut in SPSS that I had not known about.

Or sometimes they could be saving themselves some headaches.

So I thought I’d share three buttons you may not have noticed before that will make your data analysis more efficient.

[Read more…] about Three SPSS Shortcuts that Make Life Easier

Tagged With: spss shortcuts

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Member Training: Introduction to Stata Software Tutorial

by TAF Support 

In this 8-part tutorial, you will learn how to get started using Stata for data preparation, analysis, and graphing. This tutorial will give you the skills to start using Stata on your own. You will need a license to Stata and to have it installed before you begin.

[Read more…] about Member Training: Introduction to Stata Software Tutorial

Tagged With: Stata, Stata output, Statistical Software

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Member Training: Introduction to SPSS Software Tutorial

by TAF Support 

In this 10-part tutorial, you will learn how to get started using SPSS for data preparation, analysis, and graphing. This tutorial will give you the skills to start using SPSS on your own. You will need a license to SPSS and to have it installed before you begin.

[Read more…] about Member Training: Introduction to SPSS Software Tutorial

Tagged With: SPSS, SPSS menus, spss syntax, Statistical Software

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  • Getting Started with SPSS Syntax
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Member Training: R for Menu Users Software Tutorial

by TAF Support 

In this nearly 6-hour tutorial you will learn menu-based R libraries so you can use R without having to fuss with R code. These libraries don’t cover everything R can do, but they do quite a bit and can set you up to make running R much easier.

[Read more…] about Member Training: R for Menu Users Software Tutorial

Tagged With: menu-based libraries, R software, Statistical Software

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Multiple Imputation in a Nutshell

by Karen Grace-Martin  1 Comment

Updated 9/20/2021

Imputation as an approach to missing data has been around for decades.

You probably learned about mean imputation in methods classes, only to be told to never do it for a variety of very good reasons. Mean imputation, in which each missing value is replaced, or imputed, with the mean of observed values of that variable, is not the only type of imputation, however.

Two Criteria for Better Imputations

Better, although still problematic, imputation methods have two qualities. They use other variables in the data set to predict the missing value, and they contain a random component.

Using other variables preserves the relationships among variables in the imputations. It feels like cheating, but it isn’t. It ensures that any estimates of relationships using the imputed variable are not too low. Sure, underestimates are conservatively biased, but they’re still biased.

The random component is important so that all missing values of a single variable are not exactly equal. Why is that important? If all imputed values are equal, standard errors for statistics using that variable will be artificially low.

There are a few different ways to meet these criteria. One example would be to use a regression equation to predict missing values, then add a random error term.

Although this approach solves many of the problems inherent in mean imputation, one problem remains. Because the imputed value is an estimate–a predicted value–there is uncertainty about its true value. Every statistic has uncertainty, measured by its standard error. Statistics computed using imputed data have even more uncertainty than their standard errors measure.

Your statistical package cannot distinguish between an imputed value and a real value.

Since the standard errors of statistics based on imputed values are too small, corresponding p-values are also too small. P-values that are reported as smaller than they actually are? Those lead to Type I errors.

How Multiple Imputation Works

Multiple imputation solves this problem by incorporating the uncertainty inherent in imputation. It has four steps:

  1. Create m sets of imputations for the missing values using a good imputation process. This means it uses information from other variables and has a random component.
  2. The result is m full data sets. Each data set will have slightly different values for the imputed data because of the random component.
  3. Analyze each completed data set. Each set of parameter estimates will differ slightly because the data differs slightly.
  4. Combine results, calculating the variation in parameter estimates.

Remarkably, m, the number of sufficient imputations, can be only 5 to 10 imputations, although it depends on the percentage of data that are missing. A good multiple imputation model results in unbiased parameter estimates and a full sample size.

Doing multiple imputation well, however, is not always quick or easy. First, it requires that the missing data be missing at random. Second, it requires a very good imputation model. Creating a good imputation model requires knowing your data very well and having variables that will predict missing values.

Tagged With: mean imputation, Missing Data, missing data mechanism, Multiple Imputation, S-Plus, SAS, SPSS

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